Objective
Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance.
The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%.
The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks.
The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.
Fields of science
Not validated
Not validated
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- social scienceseconomics and businesseconomicsproduction economics
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
35195 Vaxjo
Sweden
See on map
Participants (16)
351 96 VAXJO
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
111 42 ATHENS ATTIKIS
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20018 Donostia
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
06000 Nice
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
97816 Lohr
See on map
20570 Bergara
See on map
31830 LAKUNTZA
See on map
35745 HERBON
See on map
080 01 Haniska
See on map
43450 LA RIBA TARRAGONA
See on map
20400 TOLOSA
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20870 Elgoibar
See on map
75015 PARIS 15
See on map
15782 Santiago De Compostela
See on map
80333 Muenchen
See on map
09111 Chemnitz
See on map